Light field reconstruction algorithms can substantially decrease the noise in stochastically rendered images with few samples per pixel. Current algorithms for defocus blur alone are both fast and accurate. However, motion blur is a considerably more complex type of camera effect, and as a consequence, current algorithms are either slow or too imprecise to use in high quality rendering.
Stochastic sampling is a powerful technique that can simulate realistic camera effects. This is achieved by evaluating a high-dimensional integral using point sampling. Motion blur is obtained by distributing samples over the open camera shutter, and depth of field by point sampling over the camera lens. However, as with most Monte Carlo techniques, a large number of samples must be drawn to reduce noise of the integral to acceptable levels. A different way to tackle this problem is to instead spend efforts on reconstructing a final image with substantially reduced noise from a sparsely sampled input image, e.g., with as few as 4 or 8 samples per pixel.
Accurately reconstructing the four-dimensional light field for depth of field is well understood. The algorithm by Vaidyanathan et al. can reconstruct images with defocus blur from a small number of samples per pixel with real-time performance. Reconstructing motion blur, and the combination of motion blur and depth of field is a harder problem, as each object and the camera can have arbitrary motion (i.e., a unique 3D motion vector for each vertex in the scene). In contrast, defocus blur is a function of the vertex depth and a few camera constants. Hence, motion blur is, in some sense, a more difficult integral to evaluate.